1. Field of the Invention
The present invention relates generally to three-dimensional modeling. More specifically, the present invention relates to a system and method for capturing three-dimensional virtual models of a site that can be co-registered and visualized within a computer system.
2. Description of Related Background Art
Lidar (light detection and ranging) uses laser technology to make precise distance measurements over long or short distances. One application of lidar is the range scanner, or scanning lidar. In a typical range scanner, a lidar is mounted on a tripod equipped with a servo mechanism that continuously pans and tilts the lidar to scan a three-dimensional area. During the scanning process, the lidar makes repeated range measurements to objects in its path. The resulting range data may be collected and serve as a rough model of the scanned area.
Physical limitations of the range scanner constrain the maximum resolution of the range data, which decreases with distance from the range scanner. At large distances, the range scanner may not be able to discern surface details of an object. A lack of continuous spatial data (gaps between points) and a lack of color attributes are significant limitations of conventional range scanners. Furthermore, a range scanner only scans objects within the lidar's line-of-sight. As a result, no data is collected for the side of an object opposite to the lidar or for objects obscured by other objects (“occlusions”).
To obtain a more complete and accurate model, the range scanner can be moved to other scanning locations in order to scan the same area from different perspectives and thereby obtain range data for obscured objects. Thereafter, the resulting sets of range data can be merged into a single model.
Unfortunately, the merging of sets of range data is not automatic. Human decision-making is generally required at several steps in the merging process. For instance, a human surveyor is typically needed to determine the relative distances between the range scanning locations and the scanned area. Furthermore, a human operator must manually identify points in common (“fiducials”) between multiple sets of range data in order to align and merge the sets into a single model. Such identification is by no means easy, particularly in the case of curved surfaces. The need for human decision-making increases the cost of modeling and the likelihood of error in the process.